Nitroreductase gene-directed enzyme prodrug therapy: insights and advances toward clinical utility
Bibliographic record
Abstract
This review examines the vast catalytic and therapeutic potential offered by type I (i.e. oxygen-insensitive) nitroreductase enzymes in partnership with nitroaromatic prodrugs, with particular focus on gene-directed enzyme prodrug therapy (GDEPT; a form of cancer gene therapy). Important first indications of this potential were demonstrated over 20 years ago, for the enzyme-prodrug pairing of Escherichia coli NfsB and CB1954 [5-(aziridin-1-yl)-2,4-dinitrobenzamide]. However, it has become apparent that both the enzyme and the prodrug in this prototypical pairing have limitations that have impeded their clinical progression. Recently, substantial advances have been made in the biodiscovery and engineering of superior nitroreductase variants, in particular development of elegant high-throughput screening capabilities to enable optimization of desirable activities via directed evolution. These advances in enzymology have been paralleled by advances in medicinal chemistry, leading to the development of second- and third-generation nitroaromatic prodrugs that offer substantial advantages over CB1954 for nitroreductase GDEPT, including greater dose-potency and enhanced ability of the activated metabolite(s) to exhibit a local bystander effect. In addition to forging substantial progress towards future clinical trials, this research is supporting other fields, most notably the development and improvement of targeted cellular ablation capabilities in small animal models, such as zebrafish, to enable cell-specific physiology or regeneration studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".